See this notebook on GitHub. You can (and should) download the project from there if you want to follow along and try this out. tl;dr: Skip to the completed example. I use blogdown to generate the websites for all the courses I teach, and it’s delightful to not have to worry about databases and server configurations.

Now that I’m on the tenure track, I’ve been looking for a way to keep track of my different research projects so I can get them all finished and published. Matt Lebo’s “Managing Your Research Pipeline” presents a neat way of quantifying and tracking the progress of your research, and I recently adopted it for my own stuff. I even made a fancy R Markdown + flexdashboard dashboard to show the status of the pipeline interactively.

GSU uses Microsoft’s Office365 for e-mail, which is fine. My previous institutions—Duke and BYU—both use it too, and it’s pretty standard. GSU also enforces 2-factor authentication (2FA) with Duo, which is also fine. Everybody should use some sort of 2FA for all their important accounts! However, for whatever reason, GSU’s version of Duo’s 2FA doesn’t allow you to generate app-specific passwords for things like e-mail.

(See this notebook on GitHub) A year ago, I wrote about how to use R to solve a typical microeconomics problem: finding the optimal price and quantity of some product given its demand and cost. Doing this involves setting the first derivatives of two functions equal to each other and using algebra to find where they cross.

(See this notebook on GitHub) Taking a sample from two groups from a population and seeing if there’s a significant or substantial difference between them is a standard task in statistics.

I am so beyond thrilled to announce that I’ll be joining the Andrew Young School of Policy Studies at Georgia State University in Fall 2019 as an assistant professor in the Department of Public Management and Policy. I’ll be teaching classes in statistics/data science, economics, and nonprofit management in beautiful downtown Atlanta, and we’ll be moving back to the South. I am so so excited about this!

This semester, I used the new ModernDive textbook to teach introductory statistics for executive MPA students at BYU, and it’s been absolutely delightful. The book’s approach to teaching statistics follows a growing trend (led by Mine Çetinkaya-Rundel, Alison Hill, and others) of emphasizing data and simulations instead of classical probability theory and complex statistical tests.

tl;dr : Skip to complete example In one of my current research projects, I use Bayesian modeling (with Stan and rstanarm) and multiple imputation (with Amelia) to measure how international aid agencies change their funding allocations to countries that impose legal restrictions on NGOs.

(See this notebook on GitHub) tl;dr : Use the functions in broomify-amelia.R to use broom::tidy(), broom::glance(), and huxtable::huxreg() on lists of multiply imputed models. The whole reason I went into the rabbit hole of the mechanics of merging imputed regression results in the previous post was so I could easily report these results in papers and writeups.

(See this notebook on GitHub) Missing data can significantly influence the results of normal regression models, since the default in R and most other statistical packages is to throw away any rows with missing variables. To avoid unnecessarily throwing out data, it’s helpful to impute missing values. One of the best ways to do this is to build a separate regression model to make predictions that fill in the gaps in data.

(See this notebook on GitHub) tl;dr : Use functions like Deriv::Deriv(), splinefun(), approxfun(), and uniroot() to do things with derivatives in R, both with actual functions and with existing empirical data A typical microeconomics problem involves finding the optimal price and quantity of a product, given its demand and cost across different quantities.